Action Recognition. As an important step towards understanding human activities, action recognition is the task to categorize human motions into predefined classes. RGB videos [1]}, [2]}, [3]}, [4]} with additional information such as optical flow and estimated poses and 3D skeletons typically obtained from RGB-D sequences [5]}, [6]}, [7]}, [8]} are the common input to existing methods.
Datasets for RGB video-based action recognition are often collected from the Internet. Some have a human-centric action design [9]}, [10]}, [11]}, [12]}, [13]}, [14]} whereas others introduce interaction and diversity in the setup [15]}, [16]}, [17]}. Recently, fine-grained action understanding [12]}, [13]}, [14]} is drawing more research attention. However, these 2D datasets lack 3D annotations. As for RGB-D datasets, earlier works are small in scale [21]}, [22]}, [23]}. As a remedy, the latest NTU RGB-D series [24]}, [25]}, [26]} features 60-120 actions. However, the majority of the actions are focused on the upper body. We develop a larger and more complete action set in HuMMan.
